






'''simple docstring'''







import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor




class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__=3   ,	UpperCamelCase__=32   ,	UpperCamelCase__=3   ,	UpperCamelCase__=10   ,	UpperCamelCase__=[10, 20, 30, 40]   ,	UpperCamelCase__=[1, 1, 2, 1]   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__="relu"   ,	UpperCamelCase__=3   ,	UpperCamelCase__=None   ,	):






         """simple docstring"""


         a_						     =			parent
         a_						     =			batch_size
         a_						     =			image_size
         a_						     =			num_channels
         a_						     =			embeddings_size
         a_						     =			hidden_sizes
         a_						     =			depths
         a_						     =			is_training
         a_						     =			use_labels
         a_						     =			hidden_act
         a_						     =			num_labels
         a_						     =			scope
         a_						     =			len(UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         a_						     =			None
         if self.use_labels:
                a_						     =			ids_tensor([self.batch_size]   ,	self.num_labels	)

         a_						     =			self.get_config()

         return config, pixel_values, labels







  def    _a       (			self	):






         """simple docstring"""


         return RegNetConfig(
             num_channels=self.num_channels   ,	embeddings_size=self.embeddings_size   ,	hidden_sizes=self.hidden_sizes   ,	depths=self.depths   ,	hidden_act=self.hidden_act   ,	num_labels=self.num_labels   ,	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			RegNetModel(config=UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(UpperCamelCase__	)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape   ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			self.num_labels
         a_						     =			RegNetForImageClassification(UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(UpperCamelCase__   ,	labels=UpperCamelCase__	)
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.prepare_config_and_inputs()
         a_     ,	a_     ,	a_						     =			config_and_inputs
         a_						     =			{'pixel_values': pixel_values}
         return config, inputs_dict





@require_torch
class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Union[str, Any]      			=     (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
  _a  : int      			=     (
      {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
      if is_torch_available()
      else {}
  )

  _a  : List[str]      			=     False
  _a  : Dict      			=     False
  _a  : Dict      			=     False
  _a  : Dict      			=     False







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			RegNetModelTester(self	)
         a_						     =			ConfigTester(self   ,	config_class=UpperCamelCase__   ,	has_text_modality=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()







  def    _a       (			self	):






         """simple docstring"""


         return







  @unittest.skip(reason='RegNet does not use inputs_embeds'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @unittest.skip(reason='RegNet does not support input and output embeddings'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                a_						     =			model_class(UpperCamelCase__	)
                a_						     =			inspect.signature(model.forward	)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                a_						     =			[*signature.parameters.keys()]

                a_						     =			['pixel_values']
                self.assertListEqual(arg_names[:1]   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                a_						     =			model_class(config=UpperCamelCase__	)
                for name, module in model.named_modules():
                       if isinstance(UpperCamelCase__   ,	(nn.BatchNormad, nn.GroupNorm)	):
                              self.assertTrue(
                                  torch.all(module.weight == 1	)   ,	msg=f'Parameter {name} of model {model_class} seems not properly initialized'   ,	)
                              self.assertTrue(
                                  torch.all(module.bias == 0	)   ,	msg=f'Parameter {name} of model {model_class} seems not properly initialized'   ,	)







  def    _a       (			self	):






         """simple docstring"""


         def check_hidden_states_output(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):
                a_						     =			model_class(UpperCamelCase__	)
                model.to(UpperCamelCase__	)
                model.eval()

                with torch.no_grad():
                       a_						     =			model(**self._prepare_for_class(UpperCamelCase__   ,	UpperCamelCase__	)	)

                a_						     =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                a_						     =			self.model_tester.num_stages
                self.assertEqual(len(UpperCamelCase__	)   ,	expected_num_stages + 1	)

                # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]	)   ,	[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,	)

         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()
         a_						     =			['basic', 'bottleneck']
         for model_class in self.all_model_classes:
                for layer_type in layers_type:
                       a_						     =			layer_type
                       a_						     =			True
                       check_hidden_states_output(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)

                       # check that output_hidden_states also work using config
                       del inputs_dict["output_hidden_states"]
                       a_						     =			True

                       check_hidden_states_output(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__	)







  @slow
  def    _a       (			self	):






         """simple docstring"""


         for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_						     =			RegNetModel.from_pretrained(UpperCamelCase__	)
                self.assertIsNotNone(UpperCamelCase__	)





def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
       return image




@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  @cached_property
  def    _a       (			self	):






         """simple docstring"""


         return (
             AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
             if is_vision_available()
             else None
         )







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(UpperCamelCase__	)

         a_						     =			self.default_image_processor
         a_						     =			prepare_img()
         a_						     =			image_processor(images=UpperCamelCase__   ,	return_tensors='pt'	).to(UpperCamelCase__	)

         # forward pass
         with torch.no_grad():
                a_						     =			model(**UpperCamelCase__	)

         # verify the logits
         a_						     =			torch.Size((1, 1_000)	)
         self.assertEqual(outputs.logits.shape   ,	UpperCamelCase__	)

         a_						     =			torch.tensor([-0.4_180, -1.5_051, -3.4_836]	).to(UpperCamelCase__	)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,	UpperCamelCase__   ,	atol=1e-4	)	)












'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  int = 1_000_000   ):
       """simple docstring"""




       a_						     =			set(range(3   ,       lowercase_   ,       2   )   )
       primes.add(2   )
       for p in range(3   ,       lowercase_   ,       2   ):
              if p not in primes:
                     continue
              primes.difference_update(set(range(p * p   ,       lowercase_   ,       lowercase_   )   )   )

       a_						     =			[float(lowercase_   ) for n in range(limit + 1   )]

       for p in primes:
              for n in range(lowercase_   ,       limit + 1   ,       lowercase_   ):
                     phi[n] *= 1 - 1 / p

       return int(sum(phi[2:]   )   )


if __name__ == "__main__":
  print(f"""{solution() = }""")




